Troubleshooting AutoAI experiments

If your AutoAI experiment fails to run successfully, review some of these common problems and resolutions.

Pipeline creation fails for binary classification

AutoAI analyzes a subset of the data to determine the best fit for experiment type. If the sample data in the prediction column contains only two values, AutoAI recommends a binary classification experiment and applies the related algorithms. If the full data set contains more than two values in the prediction column, however, the binary classification will fail and you will get an error indicating AutoAI cannot create the pipelines.

In this case, manually change the experiment type from binary to either multiclass, for a defined set of values, or regression, for an unspecified set of values.

  1. Click the Reconfigure Experiment icon to edit the experiment settings.
  2. On the Prediction page of Experiment Settings, change the prediction type to the one that best matches the data in the prediction column.
  3. Save the changes and run the experiment again.

Creating a batch deployment for an AutoAI model

You can create a batch deployment for a saved AutoAI model, but the model must be trained using the current version of Cloud Pak for Data. If it was trained using an older version, run the experiment again and deploy the resulting saved model.